A field guide to AI positions

Human data work & data labeling

Work & society.

Sources are over 18 months old.

Publication dates and source age

Sources counted: 2

Newest dated source: 2024-03-18

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

Open in the glossary Reading notes · Structured record

Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

People collect, label, check and evaluate data used in AI. Their work and the instructions they receive help shape trained software. [1] [2]

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Explanation based on the linked sources. Claim ID: claim-term-data-work-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

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Limits & distinctions

A label is a judgment made for a task, not automatically a fact. A study of AI practitioners found that neglected data work could cause problems later in development and deployment. [1] [2]

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Explanation based on the linked sources. Claim ID: claim-term-data-work-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

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A fuller explanation

Data labeling attaches categories or other annotations to examples. Wider data work includes collecting material, preparing datasets and checking model outputs. Research documents this work in different employment arrangements. [1]

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Explanation based on the linked sources. Claim ID: claim-term-data-work-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

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How it relates to the map

Map context: AI development includes labor and organizational choices, alongside model design and computing resources. [1] [2]

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Editorial interpretation or qualification. Claim ID: claim-term-data-work-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

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https://theaiatlas.org/ideas/data-work/

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Sources and what we read

  1. 1. A typology of artificial intelligence data work

    James Muldoon, Callum Cant, Boxi Wu and Mark Graham / Big Data & Society · First-hand source · Published 2024-03-18 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2024-03-18

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read abstract, fieldwork scope and data-work definitions. Includes computer-vision data workplaces and other fieldwork; not a representative census of all AI labor.

    Source ID: data-work-typology

  2. 2. Data Cascades in Machine Learning

    Nithya Sambasivan / Google Research · First-hand source · Published 2021-06-04 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2021-06-04

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the researcher's account of the interview study, examples and data-work recommendations. Findings concern the studied projects, not every AI system.

    Source ID: data-cascades-author-summary

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

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